[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1263":3,"related-1263":97},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":74,"slug":77,"view_count":78,"doi":79,"paper":80,"created_at":96},1263,"Cognitive UAV-driven agro-surveillance framework for predicting crop stress–induced yield loss using spatio-temporal learning and adaptive irrigation control","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-68174-6","Precision agriculture is becoming more and more of a challenge that requires the use of intelligent systems that are able to predict stress and prevent yield loss before it is too late. Traditional methods of agricultural surveillance are predominantly reactive with irrigation demands being based on thresholds or individual yield forecasts models that do not represent the intricate spatio-temporal interactions that exist between crop physiology, soil status, and environmental stresses. Besides, the majority of the current practices do not have an autonomous decision-making approach to preventive intervention which leads to inefficient use of water and slows down the response to stress. This paper suggests a cognitive UAV-assisted agro-surveillance system to predict yield vulnerability caused by crop stress and optimize adaptive irrigation with the help of spatio-temporal deep and reinforcement learning. The framework combines UAV-obtained RGB and multispectral and thermal imagery with measurements of soil sensors and meteorological data obtained with the Crop Health and Environmental Stress Dataset. A new GeoSpatio-TRiNet model is used to acquire long-range spatial relationship, time stress development, and diffusion of stresses across agricultural regions. The model predicts the vulnerability trajectories of the stress instead of the direct yield regression, and this allows early detection of yield risk. Such predictions serve to generate a cognitive environmental state of a Soft ActorCritic (SAC) reinforcement learning agent that autonomously computes zone-based irrigation behaviors to reduce the recurrence of stress at the minimum water usage cost. As shown by the results of the experiment, the proposed framework has a stress forecasting accuracy of 96.3% and performs much better than the traditional machine learning, CNN-based, and transformer-based baselines. The system also decreases the predicted yield vulnerability by 46.6 and enhances water-use efficiency by 41.1 as compared to irrigation strategies based on rules. The results confirm the usefulness of spatio-temporal intelligence with predictive control in terms of effectiveness, and the proposed framework is a scalable and sustainable solution to precision agriculture of the next generation.",null,"Scientific Reports","2026-08-29T00:00:00Z","论文",10,false,77,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},20,22,18,14,3,1,"提出认知无人机农业监测框架，结合时空学习与强化学习预测作物胁迫并优化灌溉，精度高且节水显著，具前沿性。",[24],{"name":9,"url":6},[26,27,28,29],"智慧农业","农业人工智能","无人机遥感","精准灌溉",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,49,56,62,68],{"code":31,"score":44,"supported":45,"title":46,"abstract":47,"tags":48},12,true,[32],[32],[29],{"code":50,"score":51,"supported":45,"title":52,"abstract":53,"tags":55},"I1",4,[],[54],"precision agriculture",[26],{"code":57,"score":51,"supported":45,"title":58,"abstract":59,"tags":61},"I2",[],[60],"machine learning",[27],{"code":63,"score":51,"supported":45,"title":64,"abstract":65,"tags":67},"I3",[],[66],"multispectral",[28],{"code":69,"score":20,"supported":45,"title":70,"abstract":71,"tags":73},"H2",[],[72],"soil",[],[75,76],"农业人工智能 无人机遥感 智慧农业 精准灌溉","农业人工智能 无人机遥感","农业人工智能无人机遥感智慧农业精准灌溉-1263",2,"10.1038\u002Fs41598-026-68174-6",{"doi":79,"openalex_id":81,"authors":82,"venue":9,"cited_by_count":87,"oa_url":88,"card":89,"direction":93,"ingested_from":95},"W7204562418",[83,85],{"name":84,"orcid":8},"S. Selvakumar",{"name":86,"orcid":8},"D. Venugopal",0,"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-68174-6_reference.pdf",{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"提出认知无人机农业监测框架，用时空学习和强化学习预测作物胁迫并优化灌溉。","结合无人机多光谱热成像、土壤传感器和气象数据，用GeoSpatio-TRiNet","胁迫预测准确率96.3%，产量脆弱性降低46.6%，水分利用效率提升41.1%。","智慧农业 \u002F 农业物联网","可探索将框架扩展到多种作物和更大尺度，或集成实时决策与自主无人机路径规划。","openalex","2026-09-01T04:03:11.723322Z",{"total":98,"page":21,"page_size":98,"items":99},6,[100,220,298,369,438,505],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":8,"source_name":106,"source_url":103,"published_at":107,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":108,"score_detail":109,"sources":112,"tags":114,"primary_topic":117,"secondary_topics":118,"classification":129,"search_phrases":190,"slug":193,"view_count":87,"doi":194,"paper":195,"created_at":219},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today","2026-10-21T00:00:00Z",79,{"impact":18,"substance":17,"depth":18,"authority":44,"freshness":110,"relevant":21,"comment":111},9,"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[113],{"name":106,"url":103},[26,27,115,116,29],"遥感","作物水分胁迫",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[119,124],{"code":57,"slug":120,"name":121,"path":122,"parent_code":123},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":125,"slug":126,"name":127,"path":128,"parent_code":123},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":38,"locked":13,"version":39,"method":40,"evidence":130},{"candidates":131,"agricultural_evidence":45},[132,138,144,151,158,163,170,177,184],{"code":31,"score":133,"supported":45,"title":134,"abstract":135,"tags":137},15,[32],[136,32],"灌溉",[29],{"code":57,"score":12,"supported":45,"title":139,"abstract":140,"tags":143},[],[141,60,142],"机器学习","artificial intelligence",[27],{"code":125,"score":110,"supported":45,"title":145,"abstract":146,"tags":150},[],[147,148,149],"物联网","internet of things","IoT",[],{"code":50,"score":152,"supported":45,"title":153,"abstract":154,"tags":157},7,[],[155,156],"数字孪生","digital twin",[26],{"code":63,"score":152,"supported":45,"title":159,"abstract":160,"tags":162},[],[115,161],"remote sensing",[115],{"code":164,"score":98,"supported":45,"title":165,"abstract":166,"tags":169},"A2",[],[167,168],"小麦","wheat",[],{"code":171,"score":98,"supported":45,"title":172,"abstract":173,"tags":176},"A3",[],[174,175],"玉米","maize",[],{"code":178,"score":98,"supported":45,"title":179,"abstract":180,"tags":183},"A5",[],[181,182],"马铃薯","potato",[],{"code":185,"score":20,"supported":45,"title":186,"abstract":187,"tags":189},"B2",[],[188],"葡萄",[],[191,192],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":194,"openalex_id":196,"authors":197,"venue":106,"cited_by_count":87,"oa_url":103,"card":213,"direction":93,"ingested_from":95},"W7223088921",[198,201,204,207,210],{"name":199,"orcid":200},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":202,"orcid":203},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":205,"orcid":206},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":208,"orcid":209},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":211,"orcid":212},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":214,"method":215,"finding":216,"direction":217,"opportunity":218},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","农业遥感与作物表型","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","2026-10-22T11:20:24.526683Z",{"id":221,"title":222,"url":223,"summary":224,"summary_zh":225,"content":8,"source_name":226,"source_url":223,"published_at":227,"category":11,"cover_url":8,"hotness":228,"is_selected":13,"score":229,"score_detail":230,"sources":235,"tags":239,"primary_topic":242,"secondary_topics":243,"classification":245,"search_phrases":275,"slug":278,"view_count":87,"doi":279,"paper":280,"created_at":297},5474,"Real-World Impact: Transforming Agriculture with Technology A Multi-Stakeholder Review of Smart Monitoring, Precision Irrigation, and Digital Value-Chain Integration","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23510500","There's a deliberate and significant transition from input to information. This article combines peer-reviewed evidence and public-sector data on six interrelated technology clusters that are fundamentally changing on-farm and off-farm decision making artificial intelligence (AI) based Farm Management Platforms, Internet of Things (IoT) smart irrigation, aerial and satellite crop monitoring, blockchain-enabled supply-chain digitisation, sustainability and carbon-tracking instruments, and farm data analytics dashboards. In the literature examined, precision tools have been correlated with 15% to 30% gains in crop yields, 30% to 55% of water saved during irrigation, and 10% to 20% of operational costs reduced, all with a range of variability based on the crop, the geographical region, and the size of the farm. It examines the mechanisms of these effects on the one hand and the differential rates of benefit, on the other, between large commercial farms and smallholders, and the policy and infrastructural conditions that drive actual adoption. It also highlights areas of current evidence which are lacking, such as limited long horizon field trials, poor interoperability standards, and lack of social outcome data. The article ends with a discussion on how to turn pilots into lasting, system-level change in food production, both from the government and enterprise side, as well as from the perspective of small and medium producers.","从投入要素到信息的转变是有意为之且意义重大的。本文结合经同行评审的证据与公共部门数据，聚焦六个相互关联、正在从根本上改变农场内外决策方式的技术集群：基于人工智能（AI）的农场管理平台、物联网（IoT）智能灌溉、航空与卫星作物监测、区块链赋能的供应链数字化、可持续性与碳追踪工具，以及农场数据分析仪表盘。在所考察的文献中，精准工具与作物产量提升15%至30%、灌溉用水节约30%至55%、运营成本降低10%至20%相关联，且这些效果因作物、地理区域和农场规模而存在不同程度的差异。本文一方面考察这些效应的作用机制，另一方面分析大型商业农场与小农户之间受益程度的差异，以及驱动实际采用的政策与基础设施条件。文章还指出了当前证据的薄弱之处，如缺乏长期田间试验、互操作性标准不完善以及社会成效数据缺失。文章最后讨论了如何从政府与企业两侧，以及中小生产者视角出发，将试点项目转化为粮食生产中持久、系统层面的变革。","Zenodo (CERN European Organization for Nuclear Research)","2026-11-25T00:00:00Z",25,73,{"impact":17,"substance":231,"depth":232,"authority":233,"freshness":87,"relevant":21,"comment":234},21,17,13,"多利益相关方综述整合六类技术集群的量化效益与采纳差异，信息增量足、产业参考价值高，但发布于一个月后，时效性不足，暂不宜进入每日精选。",[236,237],{"name":226,"url":223},{"name":226,"url":238},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[26,27,240,241,29],"农业物联网","区块链溯源",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[244],{"code":125,"slug":126,"name":127,"path":128,"parent_code":123},{"status":38,"locked":13,"version":39,"method":40,"evidence":246},{"candidates":247,"agricultural_evidence":45},[248,252,256,260,266,271],{"code":31,"score":133,"supported":45,"title":249,"abstract":250,"tags":251},[32],[136,32],[29],{"code":125,"score":12,"supported":45,"title":253,"abstract":254,"tags":255},[],[147,148,149],[240],{"code":57,"score":51,"supported":45,"title":257,"abstract":258,"tags":259},[],[142],[27],{"code":261,"score":20,"supported":45,"title":262,"abstract":263,"tags":265},"G2",[],[264],"小农户",[],{"code":267,"score":21,"supported":13,"title":268,"abstract":269,"tags":270},"E4",[],[],[241],{"code":50,"score":21,"supported":13,"title":272,"abstract":273,"tags":274},[],[],[26],[276,277],"Zenodo 智慧农业 多利益相关方综述","精准灌溉 物联网 区块链 供应链","Zenodo智慧农业多利益相关方综述-5474","10.5281\u002Fzenodo.23510500",{"doi":279,"openalex_id":281,"authors":282,"venue":226,"cited_by_count":87,"oa_url":223,"card":291,"direction":93,"ingested_from":95},"W7223034914",[283,286,288],{"name":284,"orcid":285},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":287,"orcid":8},"Dr.T.Baskar",{"name":289,"orcid":290},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":292,"method":293,"finding":294,"direction":295,"opportunity":296},"综述六大农业数字技术集群对农场决策的变革及实际效益与采纳差异。","综合同行评议证据与公共部门数据，多利益相关方综述六类技术。","精准技术可增产15-30%、节水30-55%、降本10-20%，但小农获益不均。","数字乡村与农业信息化","缺乏长期田间试验、互操作标准与社会效益数据，小农采纳机制是研究空白。","2026-10-21T23:30:08.684798Z",{"id":299,"title":300,"url":301,"summary":302,"summary_zh":303,"content":8,"source_name":304,"source_url":301,"published_at":305,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":306,"score_detail":307,"sources":309,"tags":311,"primary_topic":313,"secondary_topics":314,"classification":315,"search_phrases":346,"slug":349,"view_count":78,"doi":350,"paper":351,"created_at":368},4810,"Smart water management in agriculture: convergence of sensors, imaging, AI, precision irrigation, and robotics systems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrwa.2026.1973897","Agricultural irrigation systems can bear the heavy losses of water through runoff, evaporation, and deep percolation. Crop water requirements vary with crop species, growth stage, soil properties, evapotranspiration, weather, and plant water status; therefore, irrigation scheduling based on a single parameter is insufficient. Conventional water management systems often rely on uniform application, fixed scheduling, and limited real-time information, which overlooks spatial and temporal variation in soil, crop, and weather conditions and can result in water loss, over-irrigation, and under-irrigation. This review highlights studies on the integration of AI-driven sensing and imaging technologies, IoT-based precision irrigation, variable-rate technologies, digital twins, and agricultural robotics for crop water management. These approaches integrate field, crop, weather, and spatial information to support water-demand assessment, crop-stress monitoring, irrigation scheduling, site-specific application, and automated water management. The reviewed studies report water savings, improved water-use efficiency, crop performance, and more site-specific irrigation and input application across different crops and irrigation systems. The review also highlights the limitations of these systems, including sensing reliability, model transferability, connectivity, interoperability, cybersecurity, investment costs, and environmental effects. Overall, the integration of these technologies provides potential for timely and field-specific irrigation decisions and supports efficient and climate-resilient water management in agriculture.","农业灌溉系统会因径流、蒸发和深层渗漏而承受严重的水量损失。作物需水量随作物种类、生长阶段、土壤性质、蒸散量、天气和植株水分状况而变化；因此，仅基于单一参数的灌溉调度是不够的。传统的水管理系统往往依赖均匀灌溉、固定调度和有限的实时信息，忽视了土壤、作物和天气条件的时空变异，可能导致水分损失、过度灌溉和灌溉不足。本文综述了将人工智能驱动的传感与成像技术、基于物联网的精准灌溉、变量灌溉技术、数字孪生和农业机器人集成应用于作物水分管理的研究。这些方法整合了田块、作物、天气和空间信息，以支持需水评估、作物胁迫监测、灌溉调度、定点变量灌溉和自动化水分管理。所综述的研究报告了在不同作物和灌溉系统中实现的节水、水分利用效率提升、作物表现改善以及更具田块针对性的灌溉和投入品施用。本文还指出了这些系统的局限性，包括传感可靠性、模型可迁移性、连通性、互操作性、网络安全、投资成本和环境影响。总体而言，这些技术的集成可为及时且针对具体田块的灌溉决策提供可能，并支持农业中高效且具有气候韧性的水分管理。","Frontiers in Water","2026-10-12T00:00:00Z",78,{"impact":18,"substance":16,"depth":18,"authority":233,"freshness":110,"relevant":21,"comment":308},"系统综述AI传感、物联网精准灌溉与农业机器人集成，方法视角新、结论可靠，对智慧农业水资源管理有参考价值。",[310],{"name":304,"url":301},[26,27,312,155,29],"农业机器人",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":316},{"candidates":317,"agricultural_evidence":45},[318,323,328,332,337,342],{"code":31,"score":18,"supported":45,"title":319,"abstract":320,"tags":322},[32],[136,32,321],"evapotranspiration",[29],{"code":69,"score":98,"supported":45,"title":324,"abstract":325,"tags":327},[],[326,72],"土壤",[],{"code":125,"score":98,"supported":45,"title":329,"abstract":330,"tags":331},[],[147,149],[],{"code":50,"score":333,"supported":45,"title":334,"abstract":335,"tags":336},5,[],[155],[155,26],{"code":338,"score":51,"supported":45,"title":339,"abstract":340,"tags":341},"F6",[],[312],[312],{"code":57,"score":21,"supported":13,"title":343,"abstract":344,"tags":345},[],[],[27],[347,348],"精准灌溉 传感器 人工智能","农业机器人 水资源管理","精准灌溉传感器人工智能-4810","10.3389\u002Ffrwa.2026.1973897",{"doi":350,"openalex_id":352,"authors":353,"venue":304,"cited_by_count":87,"oa_url":362,"card":363,"direction":93,"ingested_from":95},"W7221563526",[354,357,359],{"name":355,"orcid":356},"Sachin Ghanghas","https:\u002F\u002Forcid.org\u002F0000-0001-9231-2527",{"name":358,"orcid":8},"Naveen Sihag",{"name":360,"orcid":361},"Parveen Dhanger","https:\u002F\u002Forcid.org\u002F0000-0001-5250-0275","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fwater\u002Farticles\u002F10.3389\u002Ffrwa.2026.1973897\u002Fpdf",{"tldr":364,"method":365,"finding":366,"direction":93,"opportunity":367},"综述AI传感、成像、物联网精准灌溉、数字孪生与机器人集成用于作物水分管理。","综述融合AI传感成像、IoT精准灌溉、变量技术、数字孪生与农业机器人。","集成方案可节水、提升水分利用效率与作物表现，但存在传感可靠性、模型迁移等局限。","可研究多源传感与数字孪生融合的跨区域模型迁移及低成本、安全互操作的灌溉决策系统。","2026-10-13T23:30:10.375774Z",{"id":370,"title":371,"url":372,"summary":373,"summary_zh":374,"content":8,"source_name":375,"source_url":372,"published_at":376,"category":11,"cover_url":8,"hotness":228,"is_selected":13,"score":377,"score_detail":378,"sources":381,"tags":385,"primary_topic":387,"secondary_topics":388,"classification":390,"search_phrases":416,"slug":419,"view_count":21,"doi":8,"paper":420,"created_at":437},4671,"A COMPREHENSIVE REVIEW ON IOT AND EDGE AI-BASED PRECISION AGRICULTURE SYSTEM FOR WATER OPTIMIZATION","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F316","Water scarcity and inefficient irrigation practices remain major challenges in modern agriculture, leading to reduced crop yield, resource wastage, and increased production costs. The proposed project, “IoT and Edge AI-Based Precision Agriculture System for Water Optimisation,” aims to develop an intelligent, real-time irrigation management system that optimises water usage through data-driven decision-making at the edge. The system integrates Internet of Things (IoT) sensors such as soil moisture, temperature, humidity, and light intensity sensors to continuously monitor field conditions. The collected data is processed locally using an Edge AI module, enabling real-time analysis and predictive irrigation control without relying entirely on cloud connectivity. By deploying machine learning algorithms at the edge, the system can determine optimal watering schedules based on soil conditions, crop requirements, and environmental factors, thereby minimising water wastage and ensuring efficient irrigation. A cloud platform is incorporated for long-term data storage, visualisation, and remote monitoring via a web or mobile dashboard. This hybrid architecture enhances scalability, reduces latency, and improves reliability in rural areas with limited internet connectivity. Automated irrigation control using smart valves or pumps ensures precise water delivery tailored to specific crop zones.","水资源短缺和灌溉效率低下仍是现代农业面临的主要挑战，导致作物减产、资源浪费和生产成本增加。本项目“基于物联网与边缘人工智能的水资源优化精准农业系统”旨在开发一种智能实时灌溉管理系统，通过边缘端的数据驱动决策来优化水资源利用。该系统集成了物联网（IoT）传感器，如土壤湿度、温度、湿度和光照强度传感器，以持续监测田间状况。采集的数据通过边缘人工智能模块在本地进行处理，实现实时分析和预测性灌溉控制，而无需完全依赖云连接。通过在边缘端部署机器学习算法，系统能够根据土壤条件、作物需求和环境因素确定最佳灌溉计划，从而最大限度地减少水资源浪费并确保高效灌溉。系统还包含一个云平台，用于长期数据存储、可视化以及通过网页或移动端仪表盘进行远程监控。这种混合架构增强了可扩展性，降低了延迟，并提高了互联网连接有限的农村地区的可靠性。利用智能阀门或水泵的自动灌溉控制可确保针对特定作物区域进行精准供水。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",53,{"impact":44,"substance":19,"depth":233,"authority":98,"freshness":379,"relevant":21,"comment":380},8,"综述性论文，系统梳理IoT与边缘AI在水肥优化中的应用，方法框架清晰但缺乏原创数据与实证，属细分领域参考性内容。",[382,383],{"name":375,"url":372},{"name":375,"url":384},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F315",[26,27,240,386,29],"边缘计算",{"code":125,"slug":126,"name":127,"path":128,"parent_code":123},[389],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":391},{"candidates":392,"agricultural_evidence":45},[393,398,403,408,412],{"code":125,"score":394,"supported":45,"title":395,"abstract":396,"tags":397},19,[149],[147,148,149],[240,386],{"code":50,"score":133,"supported":45,"title":399,"abstract":400,"tags":402},[54],[401,54],"精准农业",[26],{"code":31,"score":12,"supported":45,"title":404,"abstract":405,"tags":407},[],[136,406,32],"水资源",[29],{"code":57,"score":152,"supported":45,"title":409,"abstract":410,"tags":411},[],[141,60],[27],{"code":69,"score":98,"supported":45,"title":413,"abstract":414,"tags":415},[],[326,72],[],[417,418],"IoT 边缘AI 精准灌溉","智慧灌溉 土壤墒情 传感器","IoT边缘AI精准灌溉-4671",{"doi":8,"openalex_id":421,"authors":422,"venue":375,"cited_by_count":87,"oa_url":372,"card":432,"direction":93,"ingested_from":95},"W7221411136",[423,425,427,429],{"name":424,"orcid":8},"Om Gaikwad",{"name":426,"orcid":8},"Shriraj Chavan",{"name":428,"orcid":8},"Krushnadev Gaikwad",{"name":430,"orcid":431},"Swapnil R. Takale","https:\u002F\u002Forcid.org\u002F0009-0001-3636-4450",{"tldr":433,"method":434,"finding":435,"direction":93,"opportunity":436},"综述基于物联网与边缘AI的精准农业系统，实现实时灌溉优化与节水。","集成土壤湿度等IoT传感器与边缘AI，本地处理数据并云端存储可视化。","边缘AI可实时预测灌溉需求，减少水浪费并适应网络受限农村环境。","可探索边缘AI模型轻量化与多源数据融合，提升复杂农田环境下的灌溉决策精度。","2026-10-11T23:30:16.298334Z",{"id":439,"title":440,"url":441,"summary":442,"summary_zh":443,"content":8,"source_name":444,"source_url":441,"published_at":445,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":446,"score_detail":447,"sources":450,"tags":452,"primary_topic":454,"secondary_topics":455,"classification":458,"search_phrases":486,"slug":489,"view_count":51,"doi":8,"paper":490,"created_at":504},4076,"Automated System for Plant Health Monitoring and Stress Diagnosis Using IoT and Machine Learning","https:\u002F\u002Fwajes.isroset.org\u002Findex.php\u002Fj\u002Farticle\u002Fview\u002F208","Irrigation decisions on smallholder farms are still taken subjectively, and both root-zone dehydration and over-saturation measurably depress yield, making low-cost automated plant-stress diagnosis an urgent requirement in water-scarce agricultural regions. This study designs, implements and validates a decentralized system that closes the control loop between field sensing and irrigation actuation by combining Internet of Things (IoT) hardware with a cloud-hosted predictive Machine Learning (ML) model. The edge node is built on an ESP32 system-on-chip integrated with a calibrated capacitive soil-moisture probe and a DHT22 temperature and humidity array. Serialized telemetry is transmitted over a non-blocking, encrypted wireless channel to a Flask web service, where a 150-tree Random Forest classifier trained on 5,000 labelled multi-sensor records assigns the plant to one of three physiological states Optimal, Temperature Stress or Dehydration Stress and returns an actuator control bit that drives a micro-irrigation pump. An engineered Evapotranspiration Proxy Index couples ambient temperature and relative humidity into a single transpiration-sensitive feature. Capacitive calibration was validated against the gravimetric standard on ten soil cores, yielding a Pearson correlation coefficient of 0.948. Over a continuous forty-eight-hour open-air evaluation the classifier achieved an overall accuracy of 96.8%, with 98.2% precision and 97.9% recall on the critical Dehydration Stress class, and a mean end-to-end round-trip latency of 1.84 seconds. Deep-sleep duty cycling reduced the mean node current to 0.65 mA, corresponding to an estimated 159 days of unattended operation on a single 2,500 mAh cell. The results demonstrate that a low-cost edge–cloud architecture can deliver dependable, timely and energy-sustainable irrigation control for micro-precision agriculture without server-grade hardware.","小农户的灌溉决策仍依赖主观判断，而根区脱水和过度饱和均会显著降低产量，这使得低成本自动化植物胁迫诊断成为缺水农业地区的迫切需求。本研究设计、实现并验证了一种去中心化系统，通过将物联网（IoT）硬件与云端预测机器学习（ML）模型相结合，闭合了田间传感与灌溉执行之间的控制回路。边缘节点基于ESP32片上系统构建，集成了经过校准的电容式土壤水分探头和DHT22温湿度传感器阵列。序列化遥测数据通过非阻塞加密无线信道传输至Flask Web服务，在此处，一个基于5,000条带标签多传感器记录训练的150棵树的随机森林分类器将植物判定为三种生理状态之一——最优、温度胁迫或脱水胁迫——并返回驱动微灌泵的执行器控制位。所设计的蒸散代理指数将环境温度和相对湿度耦合为单一蒸腾敏感特征。电容校准在十个土芯上与重量法标准进行验证，得到皮尔逊相关系数为0.948。在连续四十八小时的户外评估中，分类器总体准确率达到96.8%，在关键的脱水胁迫类别上精确率为98.2%、召回率为97.9%，平均端到端往返延迟为1.84秒。深度睡眠占空比控制将节点平均电流降至0.65 mA，相当于在单节2,500 mAh电池上估计可无人值守运行159天。结果表明，低成本边缘—云架构无需服务器级硬件即可为微精准农业提供可靠、及时且能源可持续的灌溉控制。","World Academics Journal of Engineering Sciences","2026-09-30T00:00:00Z",75,{"impact":448,"substance":17,"depth":18,"authority":233,"freshness":98,"relevant":21,"comment":449},16,"低成本边缘-云架构实现植物胁迫自动诊断与灌溉闭环，数据扎实、结论可靠，对智慧农业节水灌溉有参考价值。",[451],{"name":444,"url":441},[26,27,240,29,453],"土壤墒情",{"code":125,"slug":126,"name":127,"path":128,"parent_code":123},[456,457],{"code":57,"slug":120,"name":121,"path":122,"parent_code":123},{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":459},{"candidates":460,"agricultural_evidence":45},[461,465,469,473,477,481],{"code":125,"score":18,"supported":45,"title":462,"abstract":463,"tags":464},[149],[147,148,149],[240],{"code":57,"score":133,"supported":45,"title":466,"abstract":467,"tags":468},[60],[141,60],[27],{"code":31,"score":12,"supported":45,"title":470,"abstract":471,"tags":472},[],[136,32,321],[29],{"code":69,"score":152,"supported":45,"title":474,"abstract":475,"tags":476},[],[326,72],[453],{"code":50,"score":152,"supported":45,"title":478,"abstract":479,"tags":480},[],[401,54],[26],{"code":261,"score":98,"supported":45,"title":482,"abstract":483,"tags":485},[],[264,484],"smallholder",[],[487,488],"ESP32 土壤湿度 灌溉 机器学习","随机森林 植物胁迫 诊断","ESP32土壤湿度灌溉机器学习-4076",{"doi":8,"openalex_id":491,"authors":492,"venue":444,"cited_by_count":87,"oa_url":441,"card":499,"direction":93,"ingested_from":95},"W7217836036",[493,495,497],{"name":494,"orcid":8},"Christian Athanas Mwakyusa",{"name":496,"orcid":8},"Allen Deogratius Mwalekwa",{"name":498,"orcid":8},"Gift Geofrey Mushi",{"tldr":500,"method":501,"finding":502,"direction":93,"opportunity":503},"设计并验证了基于ESP32与随机森林的物联网闭环灌溉系统，实现植物胁迫自动诊断。","ESP32、电容式土壤湿度传感器、DHT22、随机森林分类器、Flask云服务。","48小时测试分类准确率96.8%，脱水胁迫召回率97.9%，节点续航约159天。","可探索多作物泛化、极端环境鲁棒性及边缘端轻量化模型部署以降低云依赖。","2026-10-03T23:30:18.857006Z",{"id":506,"title":507,"url":508,"summary":509,"summary_zh":510,"content":8,"source_name":511,"source_url":508,"published_at":445,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":512,"score_detail":513,"sources":515,"tags":517,"primary_topic":520,"secondary_topics":521,"classification":522,"search_phrases":543,"slug":546,"view_count":51,"doi":547,"paper":548,"created_at":570},4036,"INTELIGÊNCIA ARTIFICIAL NA AGRICULTURA 4.0: DESAFIOS, POTENCIALIDADES E APLICAÇÕES NO MONITORAMENTO E MANEJO DAS CULTURAS","https:\u002F\u002Fdoi.org\u002F10.51891\u002Frease.v12i9.30677","This article analyzes the main applications, potentialities, and challenges of Artificial Intelligence (AI) in crop monitoring and management within the context of Agriculture 4.0. The study is characterized as a qualitative bibliographic review based on scientific publications and technical materials related to digital agriculture, artificial intelligence, and technologies applied to the agricultural sector. The analysis showed that AI has relevant applications in crop monitoring, identification of pests, diseases and weeds, irrigation management, yield prediction, and decision-making support. These technologies can contribute to more accurate crop monitoring and more rational use of agricultural resources. However, their adoption still faces challenges related to data quality and availability, implementation costs, technological infrastructure, connectivity, and user training. It is concluded that AI has the potential to make agriculture more efficient, precise, and sustainable, provided that it is integrated with technical knowledge and the actual conditions of agricultural production.","本文分析了人工智能（Artificial Intelligence, AI）在农业4.0背景下作物监测与管理中的主要应用、潜力与挑战。本研究属于基于科学出版物及数字农业、人工智能和农业领域应用技术相关技术资料的定性文献综述。分析表明，人工智能在作物监测、病虫害及杂草识别、灌溉管理、产量预测和决策支持等方面具有重要应用。这些技术有助于实现更精准的作物监测和更合理的农业资源利用。然而，其推广应用仍面临数据质量与可获得性、实施成本、技术基础设施、网络连接以及用户培训等方面的挑战。研究结论认为，只要将人工智能与技术知识和农业生产的实际条件相结合，它就有潜力使农业更加高效、精准和可持续。","Revista Ibero-Americana de Humanidades, Ciências e Educação",56,{"impact":44,"substance":19,"depth":233,"authority":110,"freshness":379,"relevant":21,"comment":514},"葡萄牙语综述论文，系统梳理AI在作物监测与管理中的应用、潜力与落地障碍，结论可靠但方法为常规文献综述，无新数据，对国内智慧农业主题页有聚合参考价值。",[516],{"name":511,"url":508},[26,27,518,29,519],"病虫害识别","作物监测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":523},{"candidates":524,"agricultural_evidence":45},[525,529,535,539],{"code":31,"score":152,"supported":45,"title":526,"abstract":527,"tags":528},[],[136,32],[29],{"code":50,"score":152,"supported":45,"title":530,"abstract":531,"tags":534},[],[532,533],"数字农业","digital agriculture",[26],{"code":57,"score":333,"supported":45,"title":536,"abstract":537,"tags":538},[],[142],[27,518],{"code":63,"score":21,"supported":13,"title":540,"abstract":541,"tags":542},[],[],[519],[544,545],"Agriculture 4.0 人工智能 作物监测","农业人工智能 病虫害识别 作物监测 智慧农业","Agriculture4.0人工智能作物监测-4036","10.51891\u002Frease.v12i9.30677",{"doi":547,"openalex_id":549,"authors":550,"venue":511,"cited_by_count":87,"oa_url":508,"card":564,"direction":295,"ingested_from":95},"W7215034853",[551,553,555,557,559,562],{"name":552,"orcid":8},"Janaina Gama Da Silva",{"name":554,"orcid":8},"Joedson Souza Da Silva",{"name":556,"orcid":8},"Levy Ribeiro Nascimento OLIVEIRA",{"name":558,"orcid":8},"Robert Rian Rodrigues Oliveira da Silva",{"name":560,"orcid":561},"Calila Teixeira Santos","https:\u002F\u002Forcid.org\u002F0000-0003-1534-7579",{"name":563,"orcid":8},"Juracir Silva Santos",{"tldr":565,"method":566,"finding":567,"direction":568,"opportunity":569},"综述人工智能在农业4.0作物监测与管理中的应用、潜力与挑战。","定性文献综述，基于数字农业与AI相关科学出版物和技术资料。","AI可用于病虫害杂草识别、灌溉、产量预测与决策，但受数据、成本、基础设施和培训制约。","农业人工智能与决策模型","可针对小农户低基础设施场景，研究轻量化AI模型与低成本数据采集方案。","2026-10-02T23:30:33.547122Z"]